Comparison of the temperature-mortality relationship in foreign born and native born died in France between 2000 and 2009

Climate Change and Cardiovascular Disease: Who Is Vulnerable?

Climate change involves a shift in earth's climate indicators over extended periods of time due to human activity. Anthropogenic air pollution has resulted in trapping heat, contributing to global warming, which contributes to worsening air pollution through facilitating oxidizing of air constituents. It is becoming more evident that the effects of climate change, such as air pollution and ambient temperatures, are interconnected with each other and other environmental factors. While the relationship between climate change components and cardiovascular disease is well documented in the literature, their interaction with one another along with individuals' biological and social risk factors is yet to be elucidated. In this review, we summarize that pathophysiological mechanisms by ambient temperatures directly affect cardiovascular health and describe the most vulnerable subgroups, defined by age, sex, race, and socioeconomic factors. Finally, we provide guidance on the importance of integrating climate, environmental, social, and health data into common platforms to inform researchers and policies.

Validation of a natural language processing algorithm using national reporting data to improve identification of anesthesia-related ADVerse evENTs: The "ADVENTURE" study

BACKGROUND

Reporting and analysis of adverse events (AE) is associated with improved health system learning, quality outcomes, and patient safety. Manual text analysis is time-consuming, costly, and prone to human errors. We aimed to demonstrate the feasibility of novel machine learning and natural language processing (NLP) approaches for early predictions of adverse events and provide input to direct quality improvement and patient safety initiatives.

METHODS

We used machine learning to analyze 9559 continuously reported AE by clinicians and healthcare systems to the French National Health accreditor (HAS) between January 1, 2009, and December 31, 2020 . We validated the labeling of 135,000 unique de-identified AE reports and determined the associations between different system's root causes and patient consequences. The model was validated by independent expert anesthesiologists.

RESULTS

The machine learning (ML) and Artificial Intelligence (AI) model trained on 9559 AE datasets accurately categorized 8800 (88%) of reported AE. The three most frequent AE types were "difficult orotracheal intubation" (16.9% of AE reports), "medication error" (10.5%), and "post-induction hypotension" (6.9%). The accuracy of the AI model reached 70.9% sensitivity, 96.6% specificity for "difficult intubation", 43.2% sensitivity, and 98.9% specificity for "medication error."

CONCLUSIONS

This unsupervised ML method provides an accurate, automated, AI-supported search algorithm that ranks and helps to understand complex risk patterns and has greater speed, precision, and clarity when compared to manual human data extraction. Machine learning and Natural language processing (NLP) models can effectively be used to process natural language AE reports and augment expert clinician input. This model can support clinical applications and methodological standards and used to better inform and enhance decision-making for improved risk management and patient safety.

TRIAL REGISTRATION

The study was approved by the ethics committee of the French Society of Anesthesiology (IRB 00010254-2020-20) and the CNIL (CNIL: 118 58 95) and the study was registered with ClinicalTrials.gov (NCT: NCT05185479).

Human tolerance to extreme heat: evidence from a desert climate population

BACKGROUND

Ambient temperatures exceeding 40 °C are projected to become common in many temperate climatic zones due to global warming. Therefore, understanding the health effects of continuous exposure to high ambient temperatures on populations living in hot climatic regions can help identify the limits of human tolerance.

OBJECTIVE

We studied the relationship between ambient temperature and non-accidental mortality in the hot desert city of Mecca, Saudi Arabia, between 2006 and 2015.

METHODS

We used a distributed lag nonlinear model to estimate the mortality-temperature association over 25 days of lag. We determined the minimum mortality temperature (MMT) and the deaths that are attributable to heat and cold.

RESULTS

We analyzed 37,178 non-accidental deaths reported in the ten-year study period among Mecca residents. The median average daily temperature was 32 °C (19-42 °C) during the same study period. We observed a U-shaped relationship between daily temperature and mortality with an MMT of 31.8 °C. The total temperature-attributable mortality of Mecca residents was 6.9% (-3.2; 14.8) without reaching statistical significance. However, extreme heat, higher than 38 °C, was significantly associated with increased risk of mortality. The lag structure effect of the temperature showed an immediate impact, followed by a decline in mortality over many days of heat. No effect of cold on mortality was observed.

IMPACT STATEMENT

High ambient temperatures are projected to become future norms in temperate climates. Studying populations familiar with desert climates for generations with access to air-conditioning would inform on the mitigation measures to protect other populations from heat and on the limits of human tolerance to extreme temperatures. We studied the relationship between ambient temperature and all-cause mortality in the hot desert city of Mecca. We found that Mecca population is adapted to high temperatures, although there was a limit to tolerance to extreme heat. This implies that mitigation measures should be directed to accelerate individual adaptation to heat and societal reorganization.

The Intersection of Immigrant and Environmental Health: A Scoping Review of Observational Population Exposure and Epidemiologic Studies

BACKGROUND

Transnational immigration has increased since the 1950s. In countries such as the United States, immigrants now account for > 15 % of the population. Although differences in health between immigrants and nonimmigrants are well documented, it is unclear how environmental exposures contribute to these disparities.

OBJECTIVES

We summarized current knowledge comparing immigrants' and nonimmigrants' exposure to and health effects of environmental exposures.

METHODS

We conducted a title and abstract review on articles identified through PubMed and selected those that assessed environmental exposures or health effects separately for immigrants and nonimmigrants. After a full text review, we extracted the main findings from eligible studies and categorized each article as exposure-focused, health-focused, or both. We also noted each study's exposure of interest, study location, exposure and statistical methods, immigrant and comparison groups, and the intersecting socioeconomic characteristics controlled for.

RESULTS

We conducted a title and abstract review on 3,705 articles, a full text review on 84, and extracted findings from 50 studies. There were 43 studies that investigated exposure (e.g., metals, organic compounds, fine particulate matter, hazardous air pollutants) disparities, but only 12 studies that assessed health disparities (e.g., mortality, select morbidities). Multiple studies reported higher exposures in immigrants compared with nonimmigrants. Among immigrants, studies sometimes observed exposure disparities by country of origin and time since immigration. Of the 50 studies, 43 were conducted in North America.

DISCUSSION

The environmental health of immigrants remains an understudied area, especially outside of North America. Although most identified studies explored potential exposure disparities, few investigated subsequent differences in health effects. Future research should investigate environmental health disparities of immigrants, especially outside North America. Additional research gaps include the role of immigrants' country of origin and time since immigration, as well as the combined effects of immigrant status with intersecting socioeconomic characteristics, such as race/ethnicity, income, and education attainment. https://doi.org/10.1289/EHP9855.

Does climatic zone of birth modify the temperature-mortality association of London inhabitants during the warm season? A time-series analysis for 2004-2013

BACKGROUND

It is known that on days with high temperatures higher mortality is observed and there is a minimum mortality temperature (MMT) point which is higher in places with warmer climate. This indicates some population adaptation to local climate but information on how quickly this adaptation will occur under climate change is lacking.

METHODS

To investigate this, we associated daily mortality data with temperature during the warm period in 2004-2013 for London inhabitants born in five climatic zones (UK, Tropical, Sub-tropical, Boreal and Mixed). We fitted Poisson regression with distributed-lag non-linear models for each climatic zone group separately to estimate group-specific exposure-response associations and MMTs. We report relative risks of death comparing the 95th percentile (21 °C) and maximum (25 °C) of the temperature distribution in London with the zone-specific minimum mortality temperature.

RESULTS

No heat-related mortality was observed for people born in countries with Sub-tropical and Mixed climates. We observed an increase of 26%, 35% and 39% in the risk of death at 25 °C compared to the MMT in people born in the UK (marine climate), Tropical and Boreal climate respectively. The temperatures with the lowest mortality in these groups ranged from 15.9 to 17.7 °C.

DISCUSSION

Our findings imply that people born in different climatic zones do not adapt fully to their new environment within their lifetime. This implies that populations may not adapt readily to climate change and will suffer increased effects from heat. In the presence of climate change, policy makers should be aware of a delayed process of adaptation.